A multiobjective optimization model for water resources planning

A technique is developed for solving multiple objective optimization programs. The approach decomposes the system into groups of objectives according to their priority in the model. A lexicographic ordering (goal programming) approach is used to analyse this system of groups, while the solution structure of each individual group is developed using the method of constraints. The technique is applied to a planning model for river basins.     

A decisional procedure for water resources planning taking into account water quality constraints

This paper describes an approach for water resources planning taking into account both quantity and quality aspects. A decisional procedure is proposed which consists of two phases. In the first phase, the water resources sharing problem is dealt with, taking into account the demands of the various water users and the requirement of a minimum flow in any section of the river. In the second phase, the problem of fulfilling water quality standards is considered. This may require to reformulate the water resources sharing problem in order to increase the water flow in the river.     

Interactive procedure for a multiobjective stochastic discrete dynamic problem

Multiple objectives and dynamics characterize many sequential decision problems. In the paper we consider returns in partially ordered criteria space as a way of generalization of single criterion dynamic programming models to multiobjective case. In our problem evaluations of alternatives with respect to criteria are represented by distribution functions. Thus, the overall comparison of two alternatives is equivalent to the comparison of two vectors of probability distributions. We assume that the decision maker tries to find a solution preferred to all other solutions (the most preferred solution). In the paper a new interactive procedure for stochastic, dynamic multiple criteria decision making problem is proposed. The procedure consists of two steps. First, the Bellman principle is used to identify the set of efficient solutions. Next interactive approach is employed to find the most preferred solution. A numerical example and a real-world application are presented to illustrate the applicability of the proposed technique.     

Robust multiobjective optimization with application to Internet routing

Robust optimization addressing decision making under uncertainty has been very well developed for problems with a single objective function and applied to areas of human activity such as portfolio selection, investment decisions, signal processing, and telecommunication-network planning. As these decision problems typically have several decisions or goals, we extend robust single objective optimization to the multiobjective case. The column-wise uncertainty model can be carried over to the multiobjective case without any additional assumptions. For the row-wise uncertainty model, we show under additional assumptions that robust efficient solutions are efficient to specific instance problems and can be found as the efficient solutions of another deterministic problem. Being motivated by the fact that Internet traffic must be maintained in a reliable yet affordable manner in situations of complex and dynamic usage, we apply the row-wise model to an intradomain multiobjective routing problem with polyhedral traffic uncertainty. We consider traditional objective functions corresponding to link utilizations and implement the biobjective case using the parametric simplex algorithm to compute robust efficient routings. We also present computational results for the Abilene network and analyze their meaning in the context of the application.     

A multiobjective decomposition-coordination framework with an application to vehicle layout and configuration design

This short paper addresses both researchers in multiobjective optimization as well as industrial practitioners and decision makers in need of solving optimization and decision problems with multiple criteria. To enhance the solution and decision process, a multiobjective decomposition-coordination framework is presented that initially decomposes the original problem into a collection of smaller-sized subproblems that can be solved for their individual solution sets. A common solution for all decomposed and, thus, the original problem is then achieved through a subsequent coordination mechanism that uses the concept of epsilon-efficiency to integrate decisions on the desired tradeoffs between these individual solutions. An application to a problem from vehicle configuration design is selected for further illustration of the results in this paper and suggests that the proposed method is an effective and promising new solution technique for multicriteria decision making and optimization. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An algorithm for a selection procedure

When we have a selection problem that involves qualitative information, we can use the qualitative programming method and find the optimal choice by using integer programming. However, in practice after formulation of the problem we have a large scale model. In this note we introduce a very effective method for solving a qualitative programming problem. The only operations used in this method are scalar and matrix addition, and hence for problems of reasonable size, the method does not need high speed computers, and can be supplemented using hand calculators.     

A multiobjective evolutionary approach for linearly constrained project selection under uncertainty

In the project selection problem a decision maker is required to allocate limited resources among an available set of competing projects. These projects could arise, although not exclusively, in an R&D, information technology or capital budgeting context. We propose an evolutionary method for project selection problems with partially funded projects, multiple (stochastic) objectives, project interdependencies (in the objectives), and a linear structure for resource constraints. The method is based on posterior articulation of preferences and is able to approximate the efficient frontier composed of stochastically nondominated solutions. We compared the method with the stochastic parameter space investigation method (PSI) and illustrate it by means of an R&D portfolio problem under uncertainty based on Monte Carlo simulation.     

A new multiobjective procedure for solving nonconvex environmental/economic power dispatch

In this article, an improved multiobjective chaotic interactive honey bee mating optimization (CIHBMO) is proposed to find the feasible optimal solution of the environmental/economic power dispatch problem with considering operational constraints of the generators. The three conflicting and noncommensurable: fuel cost, pollutant emissions, and system loss, should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multiobjective optimization problem, Pareto dominance concept is used to generate and sort the dominated and nondominated solutions. Also, fuzzy set theory is used to extract the best compromise solution. The propose method has been individually examined and applied to the standard Institute of Electrical and Electronics Engineers (IEEE) 30‐bus six generator, IEEE 180‐bus 14 generator and 40 generating unit (with valve point effect) test systems. The computational results reveal that the multiobjective CIHBMO algorithm has excellent convergence characteristics and is superior to other multiobjective optimization algorithms. Also, the result shows its great potential in handling the multiobjective problems in power systems. © 2014 Wiley Periodicals, Inc. Complexity 20: 47–62, 2014     

A system model for the utilization of mineral resources

A multiobjective dynamic optimization model is described for the optimal utilization of mineral resources. The special forms of the objective functions, state-transition relations, and additional constraints satisfy the conditions for using a special dynamic multiobjective programming method. A case study is also outlined.     

On a selection and ranking procedure for gamma populations

Summary The problem of selecting a subset of k gamma populations which includes the “best” population, i.e. the one with the largest value of the scale parameter, is studied as a multiple decision problem. The shape parameters of the gamma distributions are assumed to be known and equal for all the k populations. Based on a common number of observations from each population, a procedure R is defined which selects a subset which is never empty, small in size and yet large enough to guarantee with preassigned probability that it includes the best population regardless of the true unknown values of the scale parameters θ_i. Expression for the probability of a correct selection using R are derived and it is shown that for the case of a common number of observations the infimum of this probability is identical with the probability integral of the ratio of the maximum of k-1 independent gamma chance variables to another independent gamma chance variable, all with the same value of the other parameter. Formulas are obtained for the expected number of populations retained in the selected subset and it is shown that this function attains its maximum when the parameters θ_i are equal. Some other properties of the procedure are proved. Tables of constants b which are necessary to carry out the procedure are appended. These constants are reciprocals of the upper percentage points of F_max, the largest of several correlated F statistics. The distribution of this statistic is obtained. This work was supported in part by Office of Naval Research Contract Nonr-225 (53) at Stanford University. Reproduction in whole or in part is permitted for any purpose of the United States Government.     

A DEA based procedure for the selection of subgroups

Data Envelopment Analysis (DEA) is a nonparametric technique originally conceived for efficiency analysis of a set of units. The main characteristic of DEA based procedures is endogenous determination of weighting vectors, i.e., the weighting vectors are determined as variables of the model. Nevertheless, DEA’s applications have vastly exceeded its original target. In this paper, a DEA based model for the selection of a subgroup of alternatives or units is proposed. Considering a set of alternatives, the procedure seeks to determine the group that maximizes overall efficiency. The proposed model is characterized by free selection of weights and allows the inclusion of additional information, such as agent’s preferences in terms of relative importance of the variables under consideration or interactions between alternatives. The solution is achieved by computing a mixed-integer linear programming model. Finally, the proposed model is applied to plan the deployment of filling stations in the province of Seville (Spain).     

Optimal control of generalized multiobjective games with application to traffic networks modeling

The purpose of this paper is to study some new results on the existence and convergence of the solutions to controlled systems of generalized multiobjective games, controlled systems of traffic networks, and optimal control problems (OCPs). First, we introduce the controlled systems of generalized multiobjective games and establish the existence of the solutions for these systems using Browder-type fixed point theorem in the noncompact case and the $C_i$-quasi-concavity. Results on the convergence of controlled systems of the solutions for such problems using the auxiliary solution sets and the extended $C_i$-convexity of the objective functions are studied. Second, we investigate OCPs governed by generalized multiobjective games. The existence and convergence of the solutions to these problems are also obtained. Finally, as a real-world application, we consider the special case of controlled systems of traffic networks. Many examples are given for the illustration of our results.     

Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection

A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer programming problems.     

A two-sided Arnoldi algorithm with stopping criterion and MIMO selection procedure

In this paper we introduce a two-sided Arnoldi method for the reduction of high order linear systems and we propose useful extensions, first of all a stopping criterion to find a suitable order for the reduced model and secondly, a selection procedure to significantly improve the performance in the multi-input multi-output (MIMO) case. One application is in micro-electro-mechanical systems (MEMS). We consider a thermo-electric micro thruster model, and a comparison between the commonly used Arnoldi algorithm and the two-sided Arnoldi is performed.     

Comonotonic approximations for a generalized provisioning problem with application to optimal portfolio selection

In this paper we discuss multiperiod portfolio selection problems related to a specific provisioning problem. Our results are an extension of Dhaene et al. (2005) [14], where optimal constant mix investment strategies are obtained in a provisioning and savings context, using an analytical approach based on the concept of comonotonicity. We derive convex bounds that can be used to estimate the provision to be set up at a specified time in future, to ensure that, after having paid all liabilities up to that moment, all liabilities from that moment on can be fulfilled, with a high probability.     

Bayesian approach to global optimization and application to multiobjective and constrained problems

In this paper, the Bayesian methods of global optimization are considered. They provide the minimal expected deviation from the global minimum. It is shown that, using the Bayesian methods, the asymptotic density of calculations of the objective function is much greater around the point of global minimum. The relation of this density to the parameters of the method and to the function is defined.Algorithms are described which apply the Bayesian methods to problems with linear and nonlinear constraints. The Bayesian approach to global multiobjective optimization is defined. Interactive procedures and reduction of multidimensional data in the case of global optimization are discussed.     

Theory and application of multiobjective optimization in China — A survey

The study of multiobjective optimization (MOO) in China began at the beginning of the seventies, when some researchers in Beijing and Shanghai adopted techniques in this field to deal with practical problems arising from industrial designs and management sciences for achieving good results. Subsequently, theoretical studies began. A seminar on MOO was organized by members of the Institute of Mathematics of the Chinese Academy of Sciences in 1977. Meanwhile, studies of the theory and applications of MOO were developed in many universities. Related seminars were held and courses were offered to graduate students at these universities. At the same time, many managers and industrial designers had tried to use the techniques of this branch of OR to solve practical problems arising in a variety of fields. In 1981, 1984, 1987 and 1989, national symposiums on MOO were held, each of which had approximately 100 participants; about 300 papers and reports were presented. Up to the present time, about 200 papers on MOO by Chinese scholars have been published in China or abroad. The sphere of application becomes broader and the scale of problems being solved becomes larger. In China today, there are groups of experienced researchers working in this field, in particular at the Institute of System Sciences of the Chinese Academy of Sciences, the Jiangxi University, the Jilin Technical University, the People's University, and the Shanghai Jiao Tong University. In this paper, we will give a sketch of the results obtained by our colleagues since 1979. The paper consists of seven parts.     

A new technique to compute polygonal schema for 2-manifolds with application to null-homotopy detection

We provide a new technique for deriving optimal-sized polygonal schema for triangulated compact 2-manifolds without boundary inO(n) time, wheren is the size of the given triangulationT. We first derive a polygonal schemaP embedded inT using Seifert-Van Kampen's theorem. A reduced polygonal schemaQ of optimal size is computed fromP, where a surjective map from the vertices ofP is retained to the vertices ofQ. This helps detecting null-homotopic (contractible to a point) cycles. Given a cycle of lengthk, we determine if it is null-homotopic inO(n+k logg) time and in θ(n+k) space, whereg is the genus of the given 2-manifold. The actual contraction for a null-homotopic cycle can be computed in θ(nk) time and space. This is a considerable improvement over the previous best-known algorithm for this problem.     

A note on an iterative forward/backward scheduling technique with reference to a procedure by Li and Willis

In this note we propose an iterative scheduling technique which consists of consecutive forward/backward scheduling passes aimed at reducing the project duration by smoothing out the project's resource profile. The idea of iterative scheduling is initiated by Li and Willis in their related paper. The only common point between their scheduling technique and the one proposed here is the iterative feature. The two techniques differ both in algorithmic aspects and in the way activities are selected for scheduling at a decision point. In the technique proposed here activities are evaluated by well-reputed dispatching rules and a conflict based decision-making process called Local Constraint Based Analysis (LCBA). The results on benchmark problems from the literature demonstrate that LCBA specifically exploits the flexible activity time windows provided by the iterative scheduling technique.